An Application of Six Sigma for Optimality of Medium Density Fiberboard Production
Bibliographic record
Abstract
During the production process of MDF, there is a high level of internal bond (IB) variation. This results in the waste of out-of-standard IB values that account for 0.38 % with damage value over 1 million baht/year. The company required products with fewer volatile compounds from formaldehyde adhesives, focusing on reducing the amount of adhesive but still being strong according to IB-specification which will reduce the cost of production by about 20 − 30 million baht/year. The results of wood sampling and IB testing were divided into 6 areas, namely IB1-IB6. It was found that most of the data were symmetrical except for the IB5 data as the area where the most variation occurs. The distributions of the IB1 and IB6 data showed relatively low variability compared to data from other areas. IB1 - IB6 values were normal distribution, expect for IB5. Process capacity in IB2 was relatively high compared to IB from other areas. From the Correlation Matrix and Correlation Map, it was found that the variables that influenced the IB were Press Factor, % Dosing Glue, Heat Circuit1, Primary Circuit Intel and % Mc After Gluing. To conduct the experiment and find the best variable conditions by 25-2 - Factorial Design (Resolution: III). It was found that Glue = 7.4, Heat1 = 234.4, and Press = 6.5 would give IB = 0.88 which was closest to target (0.7). Glue = 7.1, Heat1 = 233.2, and Press = 6.48 would give IB = 1.15 which was the highest value. Results of production conditions at optimum or maximum that can be generalized from Rayleigh Method Dimensional Analysis was found that at the levels of 7.85, 254.28 and 257.70 of Glue, Heat1 and PrimCirIn, the target response (IB) was 0.7. and at the levels of 8.07, 233.35 and 281.60 of Glue, Heat1 and PrimCirIn resulted in a response value (IB) of 1.27.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".